from PIL import Image
import os, sys
path = "images/"
dirs = os.listdir( path )
def resize():
for item in dirs:
if os.path.isfile(path+item):
im = Image.open(path+item)
f, e = os.path.splitext(path+item)
imResize = im.resize((100,100), Image.ANTIALIAS)
imResize.save(f + ' resized.jpg', 'JPEG', quality=90)
resize()
from PIL import Image
#file=str(k)+'.jpg'
im = Image.open("4.jpg")
croppedIm = im.crop((0,0, 1300, 1300))
croppedIm.save('cropped_4.png')
from PIL import Image
text_file = open("data_cluster.csv", "w")
for k in range (11,12):
#file=str(k)+'.png'
file="cropped_1"+'.png'
im = Image.open(file)
im1 = im.convert('YCbCr')
im3 = im.convert('CMYK')
width, height = im.size
im2 = Image.new('RGBA', (width, height))
for i in range(0,width):
for j in range(0,height):
pixel=im.getpixel((i, j))
pixel1=im1.getpixel((i, j))
pixel2=im3.getpixel((i, j))
#print pixel[0]
#print pixel1
text_file.write('%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n'%(pixel1[0],pixel1[1],pixel1[2],pixel2[0],pixel2[1],pixel2[2],pixel[0],pixel[1],pixel[2],1))
text_file.close()
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames
import visuals as vs
%matplotlib inline
data = pd.read_csv("data_cluster.csv")
# Success - Display the first record
display(data.head(n=10))
X= data.drop('1', axis = 1)
from sklearn.decomposition import PCA
n_components = 2
pca = PCA(n_components=n_components, whiten=True).fit(X)
pca_results = vs.pca_results(X, pca)
reduced_data = pca.transform(X)
reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])
vs.biplot(X, reduced_data, pca)
clusterer = GMM(n_components=3,random_state=0).fit(reduced_data)
preds = clusterer.predict(reduced_data)
centers = clusterer.means_
indices = [10,50,80]
samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)
print "Chosen samples of wholesale customers dataset:"
samples=samples.drop('1', axis = 1)
display(samples)
pca_samples = pca.transform(samples)
vs.cluster_results(reduced_data, preds, centers,pca_samples)
import warnings
warnings.filterwarnings("ignore")
from PIL import Image
count_plant=0
count_non_plant=0
count_dead_plant=0
count=0
im = Image.open('4.jpg')
im1 = im.convert('YCbCr')
im3 = im.convert('CMYK')
width, height = im.size
im2 = Image.new('RGBA', (width, height))
imn = Image.new('RGBA', (width, height))
imm = Image.new('RGBA', (width, height))
for i in range(0,width):
for j in range(0,height):
pixel=im.getpixel((i, j))
pixel1=im1.getpixel((i, j))
pixel2=im3.getpixel((i, j))
#print pixel[0]
#print pixel1
X_image=[pixel1[0],pixel1[1],pixel1[2],pixel2[0],pixel2[1],pixel2[2],pixel[0],pixel[1],pixel[2]]
count=count+1
#text_file.write('%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n'%(pixel1[0],pixel1[1],pixel1[2],pixel2[0],pixel2[1],pixel2[2],pixel[0],pixel[1],pixel[2],1))
#if ((pixel1[0]<=255 and pixel1[0]>=210) and (pixel1[1]<=128 and pixel1[1]>=105) ):
if result==1:
im2.putpixel((i, j), (pixel[0], pixel[1], pixel[2]))
imm.putpixel((i, j), (255, 255, 255))
imn.putpixel((i, j), (255, 255, 255))
count_plant=count_plant+1
#im2.putpixel((i, j), (255, 255, 255))
elif result==2:
im2.putpixel((i, j), (255, 255, 255))
imm.putpixel((i, j), (255, 255, 255))
imn.putpixel((i, j), (pixel[0], pixel[1], pixel[2]))
count_dead_plant=count_dead_plant+1
#count_plant=count_plant+1
elif result==0:
im2.putpixel((i, j), (255, 255, 255))
imn.putpixel((i, j), (255, 255, 255))
imm.putpixel((i, j), (pixel[0], pixel[1], pixel[2]))
count_non_plant=count_non_plant=0+1
#im2.putpixel((i, j), (pixel[0], pixel[1], pixel[2]))
im2.save('newwin_cropped_4.png')
imn.save('newwin_cropped1_4.png')
imm.save('newwin_cropped1_4_4.png')
#im2.putpixel((i, j), (pixel[0], pixel[1], pixel[2]))
plant_percent=(count_plant*1.0/count*1.0)*100
Dead_plant_percent=(count_dead_plant*1.0/count*1.0)*100
LAI=(plant_percent*1.0/(100-plant_percent)*1.0)
%matplotlib inline
from PIL import Image
import matplotlib.pyplot as plt
def displayimage(image):
fig=plt.figure()
plt.imshow(image)
plt.show()
im = Image.open('1.jpg')
im2 = Image.open('newwin_cropped_1.png')
im3 = Image.open('newwin_cropped1_1.png')
im4 = Image.open('newwin_cropped1_1_1.png')
displayimage(im)
displayimage(im2)
displayimage(im3)
displayimage(im4)
print ("Plant percentage is {}".format(Dead_plant_percent))
print ("Dead Plant percentage is {}".format(plant_percent))
print ("Leaf Area Index is {}".format(LAI))
LAI=23.3/(100-23.3)
print ("Leaf Area Index of above is {}".format(LAI))
im = Image.open('7.jpg')
im2 = Image.open('newwin_cropped_7.png')
im3 = Image.open('newwin_cropped1_7.png')
im4 = Image.open('newwin_cropped1_7_7.png')
displayimage(im)
displayimage(im2)
displayimage(im3)
displayimage(im4)
print ("Plant percentage is {}".format(plant_percent))
print ("Dead Plant percentage is {}".format(Dead_plant_percent))
print ("Leaf Area Index is {}".format(LAI))
im = Image.open('11.jpg')
im2 = Image.open('newwin_cropped_11.png')
im3 = Image.open('newwin_cropped1_11.png')
im4 = Image.open('newwin_cropped1_11_11.png')
displayimage(im)
displayimage(im2)
displayimage(im3)
displayimage(im4)
print ("Plant percentage is {}".format(plant_percent))
print ("Dead Plant percentage is {}".format(Dead_plant_percent))
print ("Leaf Area Index is {}".format(LAI))
im = Image.open('13.jpg')
im2 = Image.open('newwin_cropped_13.png')
im3 = Image.open('newwin_cropped1_13.png')
im4 = Image.open('newwin_cropped1_13_13.png')
displayimage(im)
displayimage(im2)
displayimage(im3)
displayimage(im4)
print ("Plant percentage is {}".format(plant_percent))
print ("Dead Plant percentage is {}".format(Dead_plant_percent))
print ("Leaf Area Index is {}".format(LAI))